{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TPOT: A Python tool for automating data science\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com/)\n",
    "\n",
    "This notebook is a demo for the [TPOT](https://github.com/rhiever/tpot) data science automation tool under development at the [Penn Institute for Biomedical Informatics](http://upibi.org/).\n",
    "\n",
    "Below are code samples demonstrating why designing machine learning pipelines is difficult, and how TPOT automates that process.\n",
    "\n",
    "## License\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely usable and shareable as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why automate data science?\n",
    "\n",
    "### Model hyperparameter tuning is important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "   class  1  2  3  4  5  6  7  8  9 ...   775  776  777  778  779  780  781  \\\n",
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       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "mnist_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/mnist.csv.gz', sep='\\t', compression='gzip')\n",
    "mnist_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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X0nfme0CfN5mtSbTEViiO0JiZmVn0ihKhUevw/fbbDwilqKLkO4AxY8YAyW56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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cde0358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "for record_num in range(1, 65):\n",
    "    plt.subplot(8, 8, record_num)\n",
    "    \n",
    "    digit_features = mnist_data.iloc[record_num].drop('class').values\n",
    "    sb.heatmap(digit_features.reshape((28, 28)),\n",
    "               cmap='Greys',\n",
    "               square=True, cbar=False,\n",
    "               xticklabels=[], yticklabels=[])\n",
    "\n",
    "plt.tight_layout()\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.9420414   0.9620217   0.94888635  0.94000857  0.95071429  0.94427775\n",
      "  0.94955702  0.94240389  0.93467696  0.95554603]\n",
      "0.947013394966\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(RandomForestClassifier(n_estimators=10, n_jobs=-1),\n",
    "                            X=mnist_data.drop('class', axis=1).values,\n",
    "                            y=mnist_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.96445396  0.97829812  0.97015991  0.96629053  0.96828571  0.96170882\n",
      "  0.97084881  0.96698585  0.96483705  0.97641509]\n",
      "0.968828385431\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1),\n",
    "                            X=mnist_data.drop('class', axis=1).values,\n",
    "                            y=mnist_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model selection is important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X92</th>\n",
       "      <th>X93</th>\n",
       "      <th>X94</th>\n",
       "      <th>X95</th>\n",
       "      <th>X96</th>\n",
       "      <th>X97</th>\n",
       "      <th>X98</th>\n",
       "      <th>X99</th>\n",
       "      <th>X100</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1317.265789</td>\n",
       "      <td>1315.220951</td>\n",
       "      <td>1312.770581</td>\n",
       "      <td>1309.834252</td>\n",
       "      <td>1306.315588</td>\n",
       "      <td>1302.099102</td>\n",
       "      <td>1297.046401</td>\n",
       "      <td>1290.991646</td>\n",
       "      <td>1283.736109</td>\n",
       "      <td>1275.041652</td>\n",
       "      <td>...</td>\n",
       "      <td>1327.575109</td>\n",
       "      <td>1327.575350</td>\n",
       "      <td>1327.575552</td>\n",
       "      <td>1327.575719</td>\n",
       "      <td>1327.575859</td>\n",
       "      <td>1327.575976</td>\n",
       "      <td>1327.576074</td>\n",
       "      <td>1327.576155</td>\n",
       "      <td>1327.576223</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7329.967624</td>\n",
       "      <td>7379.907443</td>\n",
       "      <td>7441.799231</td>\n",
       "      <td>7518.503422</td>\n",
       "      <td>7613.565031</td>\n",
       "      <td>7731.377492</td>\n",
       "      <td>7877.385707</td>\n",
       "      <td>8058.337694</td>\n",
       "      <td>8282.596458</td>\n",
       "      <td>8560.526497</td>\n",
       "      <td>...</td>\n",
       "      <td>7121.300474</td>\n",
       "      <td>7121.300438</td>\n",
       "      <td>7121.300410</td>\n",
       "      <td>7121.300387</td>\n",
       "      <td>7121.300368</td>\n",
       "      <td>7121.300353</td>\n",
       "      <td>7121.300341</td>\n",
       "      <td>7121.300331</td>\n",
       "      <td>7121.300323</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>809.421410</td>\n",
       "      <td>809.780119</td>\n",
       "      <td>810.207191</td>\n",
       "      <td>810.715653</td>\n",
       "      <td>811.321016</td>\n",
       "      <td>812.041748</td>\n",
       "      <td>812.899834</td>\n",
       "      <td>813.921452</td>\n",
       "      <td>815.137768</td>\n",
       "      <td>816.585886</td>\n",
       "      <td>...</td>\n",
       "      <td>807.545134</td>\n",
       "      <td>807.544181</td>\n",
       "      <td>807.543381</td>\n",
       "      <td>807.542709</td>\n",
       "      <td>807.542144</td>\n",
       "      <td>807.541670</td>\n",
       "      <td>807.541272</td>\n",
       "      <td>807.540937</td>\n",
       "      <td>807.540656</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45334.208880</td>\n",
       "      <td>45334.213560</td>\n",
       "      <td>45334.219060</td>\n",
       "      <td>45334.225500</td>\n",
       "      <td>45334.233050</td>\n",
       "      <td>45334.241910</td>\n",
       "      <td>45334.252300</td>\n",
       "      <td>45334.264480</td>\n",
       "      <td>45334.278760</td>\n",
       "      <td>45334.295520</td>\n",
       "      <td>...</td>\n",
       "      <td>47550.921710</td>\n",
       "      <td>47224.457710</td>\n",
       "      <td>46946.072760</td>\n",
       "      <td>46708.686150</td>\n",
       "      <td>46506.259970</td>\n",
       "      <td>46333.645520</td>\n",
       "      <td>46186.452370</td>\n",
       "      <td>46060.936670</td>\n",
       "      <td>45953.905930</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>1.810359</td>\n",
       "      <td>...</td>\n",
       "      <td>1.790275</td>\n",
       "      <td>1.794794</td>\n",
       "      <td>1.798296</td>\n",
       "      <td>1.801010</td>\n",
       "      <td>1.803114</td>\n",
       "      <td>1.804744</td>\n",
       "      <td>1.806008</td>\n",
       "      <td>1.806987</td>\n",
       "      <td>1.807746</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             X1            X2            X3            X4            X5  \\\n",
       "0   1317.265789   1315.220951   1312.770581   1309.834252   1306.315588   \n",
       "1   7329.967624   7379.907443   7441.799231   7518.503422   7613.565031   \n",
       "2    809.421410    809.780119    810.207191    810.715653    811.321016   \n",
       "3  45334.208880  45334.213560  45334.219060  45334.225500  45334.233050   \n",
       "4      1.810359      1.810359      1.810359      1.810359      1.810359   \n",
       "\n",
       "             X6            X7            X8            X9           X10  \\\n",
       "0   1302.099102   1297.046401   1290.991646   1283.736109   1275.041652   \n",
       "1   7731.377492   7877.385707   8058.337694   8282.596458   8560.526497   \n",
       "2    812.041748    812.899834    813.921452    815.137768    816.585886   \n",
       "3  45334.241910  45334.252300  45334.264480  45334.278760  45334.295520   \n",
       "4      1.810359      1.810359      1.810359      1.810359      1.810359   \n",
       "\n",
       "   ...             X92           X93           X94           X95  \\\n",
       "0  ...     1327.575109   1327.575350   1327.575552   1327.575719   \n",
       "1  ...     7121.300474   7121.300438   7121.300410   7121.300387   \n",
       "2  ...      807.545134    807.544181    807.543381    807.542709   \n",
       "3  ...    47550.921710  47224.457710  46946.072760  46708.686150   \n",
       "4  ...        1.790275      1.794794      1.798296      1.801010   \n",
       "\n",
       "            X96           X97           X98           X99          X100  class  \n",
       "0   1327.575859   1327.575976   1327.576074   1327.576155   1327.576223      0  \n",
       "1   7121.300368   7121.300353   7121.300341   7121.300331   7121.300323      1  \n",
       "2    807.542144    807.541670    807.541272    807.540937    807.540656      1  \n",
       "3  46506.259970  46333.645520  46186.452370  46060.936670  45953.905930      1  \n",
       "4      1.803114      1.804744      1.806008      1.806987      1.807746      0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "hill_valley_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_without_noise.csv.gz', sep='\\t', compression='gzip')\n",
    "hill_valley_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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wpemPpekbTtM3lKZvODV2McNEZRE/TbVRltdEWV4bZVVjKbXloSXZQ7pcybRFa1eMtq4R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\nRSFMZ162AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d1bc240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "with plt.style.context('seaborn-notebook'):\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    for record_num in range(1, 11):\n",
    "        plt.subplot(10, 1, record_num)\n",
    "        hv_record_features = hill_valley_data.loc[record_num].drop('class').values\n",
    "        plt.plot(hv_record_features)\n",
    "        plt.yticks([])\n",
    "        plt.xticks([])\n",
    "\n",
    "    plt.tight_layout()\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.6147541   0.63934426  0.62809917  0.60330579  0.6446281   0.5785124\n",
      "  0.66942149  0.55371901  0.49586777  0.66115702]\n",
      "0.608880910446\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1),\n",
    "                            X=hill_valley_data.drop('class', axis=1).values,\n",
    "                            y=hill_valley_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.          1.          0.99173554  1.          1.          1.          1.\n",
      "  0.99173554  1.          1.        ]\n",
      "0.998347107438\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(LogisticRegression(),\n",
    "                            X=hill_valley_data.drop('class', axis=1).values,\n",
    "                            y=hill_valley_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature preprocessing is important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X92</th>\n",
       "      <th>X93</th>\n",
       "      <th>X94</th>\n",
       "      <th>X95</th>\n",
       "      <th>X96</th>\n",
       "      <th>X97</th>\n",
       "      <th>X98</th>\n",
       "      <th>X99</th>\n",
       "      <th>X100</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.02</td>\n",
       "      <td>36.49</td>\n",
       "      <td>38.20</td>\n",
       "      <td>38.85</td>\n",
       "      <td>39.38</td>\n",
       "      <td>39.74</td>\n",
       "      <td>37.02</td>\n",
       "      <td>39.53</td>\n",
       "      <td>38.81</td>\n",
       "      <td>38.79</td>\n",
       "      <td>...</td>\n",
       "      <td>36.62</td>\n",
       "      <td>36.92</td>\n",
       "      <td>38.80</td>\n",
       "      <td>38.52</td>\n",
       "      <td>38.07</td>\n",
       "      <td>36.73</td>\n",
       "      <td>39.46</td>\n",
       "      <td>37.50</td>\n",
       "      <td>39.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.83</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.78</td>\n",
       "      <td>...</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68177.69</td>\n",
       "      <td>66138.42</td>\n",
       "      <td>72981.88</td>\n",
       "      <td>74304.33</td>\n",
       "      <td>67549.66</td>\n",
       "      <td>69367.34</td>\n",
       "      <td>69169.41</td>\n",
       "      <td>73268.61</td>\n",
       "      <td>74465.84</td>\n",
       "      <td>72503.37</td>\n",
       "      <td>...</td>\n",
       "      <td>73438.88</td>\n",
       "      <td>71053.35</td>\n",
       "      <td>71112.62</td>\n",
       "      <td>74916.48</td>\n",
       "      <td>72571.58</td>\n",
       "      <td>66348.97</td>\n",
       "      <td>71063.72</td>\n",
       "      <td>67404.27</td>\n",
       "      <td>74920.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44889.06</td>\n",
       "      <td>39191.86</td>\n",
       "      <td>40728.46</td>\n",
       "      <td>38576.36</td>\n",
       "      <td>45876.06</td>\n",
       "      <td>47034.00</td>\n",
       "      <td>46611.43</td>\n",
       "      <td>37668.32</td>\n",
       "      <td>40980.89</td>\n",
       "      <td>38466.15</td>\n",
       "      <td>...</td>\n",
       "      <td>42625.67</td>\n",
       "      <td>40684.20</td>\n",
       "      <td>46960.73</td>\n",
       "      <td>44546.80</td>\n",
       "      <td>45410.53</td>\n",
       "      <td>47139.44</td>\n",
       "      <td>43095.68</td>\n",
       "      <td>40888.34</td>\n",
       "      <td>39615.19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.70</td>\n",
       "      <td>5.40</td>\n",
       "      <td>5.28</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.27</td>\n",
       "      <td>5.61</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.87</td>\n",
       "      <td>...</td>\n",
       "      <td>5.17</td>\n",
       "      <td>5.67</td>\n",
       "      <td>5.60</td>\n",
       "      <td>5.94</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.22</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2        X3        X4        X5        X6        X7  \\\n",
       "0     39.02     36.49     38.20     38.85     39.38     39.74     37.02   \n",
       "1      1.83      1.71      1.77      1.77      1.68      1.78      1.80   \n",
       "2  68177.69  66138.42  72981.88  74304.33  67549.66  69367.34  69169.41   \n",
       "3  44889.06  39191.86  40728.46  38576.36  45876.06  47034.00  46611.43   \n",
       "4      5.70      5.40      5.28      5.38      5.27      5.61      6.00   \n",
       "\n",
       "         X8        X9       X10  ...         X92       X93       X94  \\\n",
       "0     39.53     38.81     38.79  ...       36.62     36.92     38.80   \n",
       "1      1.70      1.75      1.78  ...        1.80      1.79      1.77   \n",
       "2  73268.61  74465.84  72503.37  ...    73438.88  71053.35  71112.62   \n",
       "3  37668.32  40980.89  38466.15  ...    42625.67  40684.20  46960.73   \n",
       "4      5.38      5.34      5.87  ...        5.17      5.67      5.60   \n",
       "\n",
       "        X95       X96       X97       X98       X99      X100  class  \n",
       "0     38.52     38.07     36.73     39.46     37.50     39.10      0  \n",
       "1      1.74      1.74      1.80      1.78      1.75      1.69      1  \n",
       "2  74916.48  72571.58  66348.97  71063.72  67404.27  74920.24      1  \n",
       "3  44546.80  45410.53  47139.44  43095.68  40888.34  39615.19      0  \n",
       "4      5.94      5.73      5.22      5.30      5.73      5.91      0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "hill_valley_noisy_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_with_noise.csv.gz', sep='\\t', compression='gzip')\n",
    "hill_valley_noisy_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WFF+9vICco/vxSVlJYVS36+kfHSM2PIgf3FRKSVb0jCdpapyWXVWDdI/YGBy188x7LVjG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4vfP1dPc6+ZMI0Sk83D0JiDxfOTZv3dRFHkme2d7KgdZsTkIjxURW5q1NR2Ob3873N1DBudqJXy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\nLBENlIiIiIjIsuT/AwYuWcOKMBlBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ce6ecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "with plt.style.context('seaborn-notebook'):\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    for record_num in range(1, 11):\n",
    "        plt.subplot(10, 1, record_num)\n",
    "        hv_noisy_record_features = hill_valley_noisy_data.loc[record_num].drop('class').values\n",
    "        plt.plot(hv_noisy_record_features)\n",
    "        plt.yticks([])\n",
    "        plt.xticks([])\n",
    "\n",
    "    plt.tight_layout()\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.53278689  0.53278689  0.55737705  0.59016393  0.62295082  0.55737705\n",
      "  0.58333333  0.55833333  0.6         0.56666667]\n",
      "0.570177595628\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1),\n",
    "                            X=hill_valley_noisy_data.drop('class', axis=1).values,\n",
    "                            y=hill_valley_noisy_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.95901639  0.98360656  0.87704918  0.98360656  0.92622951  0.93442623\n",
      "  0.90833333  0.925       0.95833333  0.96666667]\n",
      "0.942226775956\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(make_pipeline(PCA(n_components=10), RandomForestClassifier(n_estimators=100, n_jobs=-1)),\n",
    "                            X=hill_valley_noisy_data.drop('class', axis=1).values,\n",
    "                            y=hill_valley_noisy_data.loc[:, 'class'].values,\n",
    "                            cv=10)\n",
    "\n",
    "print(cv_scores)\n",
    "print(np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automating data science with TPOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
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       "      <th>X92</th>\n",
       "      <th>X93</th>\n",
       "      <th>X94</th>\n",
       "      <th>X95</th>\n",
       "      <th>X96</th>\n",
       "      <th>X97</th>\n",
       "      <th>X98</th>\n",
       "      <th>X99</th>\n",
       "      <th>X100</th>\n",
       "      <th>class</th>\n",
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       "  </thead>\n",
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       "      <td>36.49</td>\n",
       "      <td>38.20</td>\n",
       "      <td>38.85</td>\n",
       "      <td>39.38</td>\n",
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       "      <td>38.81</td>\n",
       "      <td>38.79</td>\n",
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       "      <td>36.92</td>\n",
       "      <td>38.80</td>\n",
       "      <td>38.52</td>\n",
       "      <td>38.07</td>\n",
       "      <td>36.73</td>\n",
       "      <td>39.46</td>\n",
       "      <td>37.50</td>\n",
       "      <td>39.10</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.77</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.78</td>\n",
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       "      <td>1.80</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>74304.33</td>\n",
       "      <td>67549.66</td>\n",
       "      <td>69367.34</td>\n",
       "      <td>69169.41</td>\n",
       "      <td>73268.61</td>\n",
       "      <td>74465.84</td>\n",
       "      <td>72503.37</td>\n",
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       "      <td>73438.88</td>\n",
       "      <td>71053.35</td>\n",
       "      <td>71112.62</td>\n",
       "      <td>74916.48</td>\n",
       "      <td>72571.58</td>\n",
       "      <td>66348.97</td>\n",
       "      <td>71063.72</td>\n",
       "      <td>67404.27</td>\n",
       "      <td>74920.24</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>44889.06</td>\n",
       "      <td>39191.86</td>\n",
       "      <td>40728.46</td>\n",
       "      <td>38576.36</td>\n",
       "      <td>45876.06</td>\n",
       "      <td>47034.00</td>\n",
       "      <td>46611.43</td>\n",
       "      <td>37668.32</td>\n",
       "      <td>40980.89</td>\n",
       "      <td>38466.15</td>\n",
       "      <td>...</td>\n",
       "      <td>42625.67</td>\n",
       "      <td>40684.20</td>\n",
       "      <td>46960.73</td>\n",
       "      <td>44546.80</td>\n",
       "      <td>45410.53</td>\n",
       "      <td>47139.44</td>\n",
       "      <td>43095.68</td>\n",
       "      <td>40888.34</td>\n",
       "      <td>39615.19</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5.70</td>\n",
       "      <td>5.40</td>\n",
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       "      <td>5.38</td>\n",
       "      <td>5.27</td>\n",
       "      <td>5.61</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.38</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.87</td>\n",
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       "      <td>5.17</td>\n",
       "      <td>5.67</td>\n",
       "      <td>5.60</td>\n",
       "      <td>5.94</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.22</td>\n",
       "      <td>5.30</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.91</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 101 columns</p>\n",
       "</div>"
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       "         X1        X2        X3        X4        X5        X6        X7  \\\n",
       "0     39.02     36.49     38.20     38.85     39.38     39.74     37.02   \n",
       "1      1.83      1.71      1.77      1.77      1.68      1.78      1.80   \n",
       "2  68177.69  66138.42  72981.88  74304.33  67549.66  69367.34  69169.41   \n",
       "3  44889.06  39191.86  40728.46  38576.36  45876.06  47034.00  46611.43   \n",
       "4      5.70      5.40      5.28      5.38      5.27      5.61      6.00   \n",
       "\n",
       "         X8        X9       X10  ...         X92       X93       X94  \\\n",
       "0     39.53     38.81     38.79  ...       36.62     36.92     38.80   \n",
       "1      1.70      1.75      1.78  ...        1.80      1.79      1.77   \n",
       "2  73268.61  74465.84  72503.37  ...    73438.88  71053.35  71112.62   \n",
       "3  37668.32  40980.89  38466.15  ...    42625.67  40684.20  46960.73   \n",
       "4      5.38      5.34      5.87  ...        5.17      5.67      5.60   \n",
       "\n",
       "        X95       X96       X97       X98       X99      X100  class  \n",
       "0     38.52     38.07     36.73     39.46     37.50     39.10      0  \n",
       "1      1.74      1.74      1.80      1.78      1.75      1.69      1  \n",
       "2  74916.48  72571.58  66348.97  71063.72  67404.27  74920.24      1  \n",
       "3  44546.80  45410.53  47139.44  43095.68  40888.34  39615.19      0  \n",
       "4      5.94      5.73      5.22      5.30      5.73      5.91      0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from tpot import TPOTClassifier\n",
    "\n",
    "hill_valley_noisy_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_with_noise.csv.gz', sep='\\t', compression='gzip')\n",
    "hill_valley_noisy_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:   9%|▉         | 100/1100 [00:00<36:47,  2.21s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current best internal CV score: 0.9472727662812096\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  18%|█▊        | 201/1100 [07:15<50:37,  3.38s/pipeline]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 2 - Current best internal CV score: 0.9495601844270386\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  27%|██▋       | 301/1100 [08:12<04:03,  3.29pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 3 - Current best internal CV score: 0.9495601844270386\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  36%|███▋      | 401/1100 [09:28<05:49,  2.00pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 4 - Current best internal CV score: 0.9495601844270386\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  46%|████▌     | 501/1100 [12:45<21:05,  2.11s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 5 - Current best internal CV score: 0.9495601844270386\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  54%|█████▍    | 596/1100 [00:00<01:19,  6.31pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 6 - Current best internal CV score: 0.950678752659701\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  63%|██████▎   | 695/1100 [00:00<02:43,  2.47pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 7 - Current best internal CV score: 0.9529158891250255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  73%|███████▎  | 801/1100 [16:28<01:18,  3.83pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 8 - Current best internal CV score: 0.9529158891250255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  80%|████████  | 884/1100 [00:00<02:46,  1.30pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 9 - Current best internal CV score: 0.9529158891250255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  91%|█████████ | 1001/1100 [19:09<00:31,  3.17pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 10 - Current best internal CV score: 0.9529158891250255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best pipeline: LogisticRegression(input_matrix, 0.57999999999999996, 25, False)\n",
      "0.983631993007\n"
     ]
    }
   ],
   "source": [
    "X = hill_valley_noisy_data.drop('class', axis=1).values\n",
    "y = hill_valley_noisy_data.loc[:, 'class'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25)\n",
    "\n",
    "my_tpot = TPOTClassifier(generations=10, verbosity=2)\n",
    "my_tpot.fit(X_train, y_train)\n",
    "print(my_tpot.score(X_test, y_test))"
   ]
  }
 ],
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